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Modeling Forest Landscapes: Parameter Estimation from Gap Models over Heterogeneous Terrain.

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Title: Modeling Forest Landscapes: Parameter Estimation from Gap Models over Heterogeneous Terrain.
Authors: Acevedo, Miguel F.1,2, Pamarti, Siva3, Ablan, Magdiel2, Urban, Dean L.4, Mikler, Armin3
Source: Simulation. Jul2001, Vol. 77 Issue 1/2, p53-68. 16p.
Subjects: Forests & forestry, Landscapes, Markov processes, Probability theory, Density functionals, Methodology
Abstract: Parameter values of a forest landscape model (MO SAIC) are estimated from a terrain sensitive gap model (FACET) over a large number of terrain types. MOSAIC is a semi-Markov model with states defined by cover types. For each terrain type, gap-model output is fed to a program that counts transitions between each pair of states, and estimates the fixed lags and the parameters of the probability density functions of the distributed delays. The gap model and the parameter estimator are executed repetitively for many different steps in the gradients and many ter rain types, taking advantage of a computer cluster running a distributed launching system. The method is illustrated by its application to the H.J. Andrews Forest in the Oregon Cascades. Key transitions in cover type determine the variation of the landscape- model parameters over all terrain types. The method ology presented here provides an automated, consis tent and conceptually clear procedure for scaling up the tree level ecological detail represented by a gap model, to the level ofa patch state-transition model for heterogeneous environmental conditions across the landscape. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
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Abstract:Parameter values of a forest landscape model (MO SAIC) are estimated from a terrain sensitive gap model (FACET) over a large number of terrain types. MOSAIC is a semi-Markov model with states defined by cover types. For each terrain type, gap-model output is fed to a program that counts transitions between each pair of states, and estimates the fixed lags and the parameters of the probability density functions of the distributed delays. The gap model and the parameter estimator are executed repetitively for many different steps in the gradients and many ter rain types, taking advantage of a computer cluster running a distributed launching system. The method is illustrated by its application to the H.J. Andrews Forest in the Oregon Cascades. Key transitions in cover type determine the variation of the landscape- model parameters over all terrain types. The method ology presented here provides an automated, consis tent and conceptually clear procedure for scaling up the tree level ecological detail represented by a gap model, to the level ofa patch state-transition model for heterogeneous environmental conditions across the landscape. [ABSTRACT FROM PUBLISHER]
ISSN:00375497
DOI:10.1177/003754970107700104